Chronic alcohol causes widespread changes in brain gene expression in humans and animal models, some of which contribute to alcohol addiction and alcohol dependence. Recent studies point to a central role of chromatin modifications, often referred to as epigenetic changes, in controlling alcohol- induced changes in gene expression and behavior. To understand how chromatin modifications mediate changes in gene expression in alcoholic brain, an integration of chromatin and transcriptional data at the genome level is required. Here we will test the hypothesis that chronic alcohol abuse changes gene expression via long-lasting changes in chromatin states. We will first measure genomic differences at two chromatin marks in postmortem brains of chronic alcoholics and control cases using chromatin immunoprecipitation followed by DNA sequencing (ChIP-Seq). We will then explore the genome-wide relationships between chromatin modifications and gene expression measured in the same samples by RNA-Seq using a novel systems approach. This approach will allow us to partition genomic variance into biologically meaningful patterns and identify robust correlations between ChIP- Seq and RNA-Seq data sets, which we will use to propose mechanistic links between chromatin marks and gene expression. This approach will also prioritize individual genes based on their importance in gene networks and correlations with chromatin marks and we will use this strategy to select several hub genes for validation. We will validate chromatin marks of several hub genes with ChIP followed by qRT- PCR and their expression levels with qRT-PCR. Overall, this proposal aims to develop a systems approach that will detect robust co-variation between ChIP-Seq and RNA-Seq data sets. We will identify epigenetic components critical for regulation of gene expression by chronic alcohol abuse and provide new targets for medication development for human alcoholism. In addition, this approach may serve as a prototype for analysis of the wealth of existing and emerging genomic data.